Comprehensive evaluation of the WaterGAP3 model across climatic, physiographic, and anthropogenic gradients

In den letzten Jahrzehnten haben sich makroskalige hydrologische Modelle als wichtige Werkzeuge etabliert um den Zustand der globalen erneuerbaren Süßwasserressourcen flächendeckend bewerten können. Sie werden heutzutage eingesetzt um eine große Bandbreite wissenschaftlicher Fragestellungen zu beantworten, insbesondere hinsichtlich der Auswirkungen anthropogener Einflüsse auf das natürliche Abflussregime oder der Auswirkungen des globalen Wandels und Klimawandels auf die Ressource Wasser. Diese Auswirkungen lassen sich durch verschiedenste wasserbezogene Kenngrößen abschätzen, wie z.B. erneuerbare (Grund-)Wasserressourcen, Hochwasserrisiko, Dürren, Wasserstress und Wasserknappheit. Die Weiterentwicklung makroskaliger hydrologischer Modelle wurde insbesondere durch stetig steigende Rechenkapazitäten begünstigt, aber auch durch die zunehmende Verfügbarkeit von Fernerkundungsdaten und abgeleiteten Datenprodukten, die genutzt werden können, um die Modelle anzutreiben und zu verbessern. Wie alle makro- bis globalskaligen Modellierungsansätze unterliegen makroskalige hydrologische Simulationen erheblichen Unsicherheiten, die (i) auf räumliche Eingabedatensätze, wie z.B. meteorologische Größen oder Landoberflächenparameter, und (ii) im Besonderen auf die (oftmals) vereinfachte Abbildung physikalischer Prozesse im Modell zurückzuführen sind. Angesichts dieser Unsicherheiten ist es unabdingbar, die tatsächliche Anwendbarkeit und Prognosefähigkeit der Modelle unter diversen klimatischen und physiographischen Bedingungen zu überprüfen. Bisher wurden die meisten Evaluierungsstudien jedoch lediglich in wenigen, großen Flusseinzugsgebieten durchgeführt oder fokussierten auf kontinentalen Wasserflüssen. Dies steht im Kontrast zu vielen Anwendungsstudien, deren Analysen und Aussagen auf simulierten Zustandsgrößen und Flüssen in deutlich feinerer räumlicher Auflösung (Gridzelle) basieren. Den Kern der Dissertation bildet eine umfangreiche Evaluierung der generellen Anwendbarkeit des globalen hydrologischen Modells WaterGAP3 für die Simulation von monatlichen Abflussregimen und Niedrig- und Hochwasserabflüssen auf Basis von mehr als 2400 Durchflussmessreihen für den Zeitraum 1958-2010. Die betrachteten Flusseinzugsgebiete repräsentieren ein breites Spektrum klimatischer und physiographischer Bedingungen, die Einzugsgebietsgröße reicht von 3000 bis zu mehreren Millionen Quadratkilometern. Die Modellevaluierung hat dabei zwei Zielsetzungen: Erstens soll die erzielte Modellgüte als Bezugswert dienen gegen den jegliche weiteren Modellverbesserungen verglichen werden können. Zweitens soll eine Methode zur diagnostischen Modellevaluierung entwickelt und getestet werden, die eindeutige Ansatzpunkte zur Modellverbesserung aufzeigen soll, falls die Modellgüte unzureichend ist. Hierzu werden komplementäre Modellgütemaße mit neun Gebietsparametern verknüpft, welche die klimatischen und physiographischen Bedingungen sowie den Grad anthropogener Beeinflussung in den einzelnen Einzugsgebieten quantifizieren. WaterGAP3 erzielt eine mittlere bis hohe Modellgüte für die Simulation von sowohl monatlichen Abflussregimen als auch Niedrig- und Hochwasserabflüssen, jedoch sind für alle betrachteten Modellgütemaße deutliche räumliche Muster erkennbar. Von den neun betrachteten Gebietseigenschaften weisen insbesondere der Ariditätsgrad und die mittlere Gebietsneigung einen starken Einfluss auf die Modellgüte auf. Das Modell tendiert zur Überschätzung des jährlichen Abflussvolumens mit steigender Aridität. Dieses Verhalten ist charakteristisch für makroskalige hydrologische Modelle und ist auf die unzureichende Abbildung von Prozessen der Abflussbildung und –konzentration in wasserlimitierten Gebieten zurückzuführen. In steilen Einzugsgebieten wird eine geringe Modellgüte hinsichtlich der Abbildung von monatlicher Abflussvariabilität und zeitlicher Dynamik festgestellt, die sich auch in der Güte der Niedrig- und Hochwassersimulation widerspiegelt. Diese Beobachtung weist auf notwendige Modellverbesserungen in Bezug auf (i) die Aufteilung des Gesamtabflusses in schnelle und verzögerte Abflusskomponente und (ii) die Berechnung der Fließgeschwindigkeit im Gerinne hin. Die im Rahmen der Dissertation entwickelte Methode zur diagnostischen Modellevaluierung durch Verknüpfung von komplementären Modellgütemaßen und Einzugsgebietseigenschaften wurde exemplarisch am Beispiel des WaterGAP3 Modells erprobt. Die Methode hat sich als effizientes Werkzeug erwiesen, um räumliche Muster in der Modellgüte zu erklären und Defizite in der Modellstruktur zu identifizieren. Die entwickelte Methode ist generell für jedes hydrologische Modell anwendbar. Sie ist jedoch insbesondere für makroskalige Modelle und multi-basin Studien relevant, da sie das Fehlen von feldspezifischen Kenntnissen und gezielten Messkampagnen, auf die üblicherweise in der Einzugsgebietsmodellierung zurückgegriffen wird, teilweise ausgleichen kann.

Bayes quadratic unbiased estimator of spatial covariance parameters

This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.

A Modeling Analysis of Changing Global Land Use as affected by Changing Demands for Wood Products

The 21st century has brought new challenges for forest management at a time when globalization in world trade is increasing and global climate change is becoming increasingly apparent. In addition to various goods and services like food, feed, timber or biofuels being provided to humans, forest ecosystems are a large store of terrestrial carbon and account for a major part of the carbon exchange between the atmosphere and the land surface. Depending on the stage of the ecosystems and/or management regimes, forests can be either sinks, or sources of carbon. At the global scale, rapid economic development and a growing world population have raised much concern over the use of natural resources, especially forest resources. The challenging question is how can the global demands for forest commodities be satisfied in an increasingly globalised economy, and where could they potentially be produced? For this purpose, wood demand estimates need to be integrated in a framework, which is able to adequately handle the competition for land between major land-use options such as residential land or agricultural land. This thesis is organised in accordance with the requirements to integrate the simulation of forest changes based on wood extraction in an existing framework for global land-use modelling called LandSHIFT. Accordingly, the following neuralgic points for research have been identified: (1) a review of existing global-scale economic forest sector models (2) simulation of global wood production under selected scenarios (3) simulation of global vegetation carbon yields and (4) the implementation of a land-use allocation procedure to simulate the impact of wood extraction on forest land-cover. Modelling the spatial dynamics of forests on the global scale requires two important inputs: (1) simulated long-term wood demand data to determine future roundwood harvests in each country and (2) the changes in the spatial distribution of woody biomass stocks to determine how much of the resource is available to satisfy the simulated wood demands. First, three global timber market models are reviewed and compared in order to select a suitable economic model to generate wood demand scenario data for the forest sector in LandSHIFT. The comparison indicates that the ‘Global Forest Products Model’ (GFPM) is most suitable for obtaining projections on future roundwood harvests for further study with the LandSHIFT forest sector. Accordingly, the GFPM is adapted and applied to simulate wood demands for the global forestry sector conditional on selected scenarios from the Millennium Ecosystem Assessment and the Global Environmental Outlook until 2050. Secondly, the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model is utilized to simulate the change in potential vegetation carbon stocks for the forested locations in LandSHIFT. The LPJ data is used in collaboration with spatially explicit forest inventory data on aboveground biomass to allocate the demands for raw forest products and identify locations of deforestation. Using the previous results as an input, a methodology to simulate the spatial dynamics of forests based on wood extraction is developed within the LandSHIFT framework. The land-use allocation procedure specified in the module translates the country level demands for forest products into woody biomass requirements for forest areas, and allocates these on a five arc minute grid. In a first version, the model assumes only actual conditions through the entire study period and does not explicitly address forest age structure. Although the module is in a very preliminary stage of development, it already captures the effects of important drivers of land-use change like cropland and urban expansion. As a first plausibility test, the module performance is tested under three forest management scenarios. The module succeeds in responding to changing inputs in an expected and consistent manner. The entire methodology is applied in an exemplary scenario analysis for India. A couple of future research priorities need to be addressed, particularly the incorporation of plantation establishments; issue of age structure dynamics; as well as the implementation of a new technology change factor in the GFPM which can allow the specification of substituting raw wood products (especially fuelwood) by other non-wood products.

The prediction of seasonal and inter-annual climate variations and their impacts on the water resources in the eastern seaboard of Thailand

The research of this thesis dissertation covers developments and applications of short-and long-term climate predictions. The short-term prediction emphasizes monthly and seasonal climate, i.e. forecasting from up to the next month over a season to up to a year or so. The long-term predictions pertain to the analysis of inter-annual- and decadal climate variations over the whole 21st century. These two climate prediction methods are validated and applied in the study area, namely, Khlong Yai (KY) water basin located in the eastern seaboard of Thailand which is a major industrial zone of the country and which has been suffering from severe drought and water shortage in recent years. Since water resources are essential for the further industrial development in this region, a thorough analysis of the potential climate change with its subsequent impact on the water supply in the area is at the heart of this thesis research. The short-term forecast of the next-season climate, such as temperatures and rainfall, offers a potential general guideline for water management and reservoir operation. To that avail, statistical models based on autoregressive techniques, i.e., AR-, ARIMA- and ARIMAex-, which includes additional external regressors, and multiple linear regression- (MLR) models, are developed and applied in the study region. Teleconnections between ocean states and the local climate are investigated and used as extra external predictors in the ARIMAex- and the MLR-model and shown to enhance the accuracy of the short-term predictions significantly. However, as the ocean state – local climate teleconnective relationships provide only a one- to four-month ahead lead time, the ocean state indices can support only a one-season-ahead forecast. Hence, GCM- climate predictors are also suggested as an additional predictor-set for a more reliable and somewhat longer short-term forecast. For the preparation of “pre-warning” information for up-coming possible future climate change with potential adverse hydrological impacts in the study region, the long-term climate prediction methodology is applied. The latter is based on the downscaling of climate predictions from several single- and multi-domain GCMs, using the two well-known downscaling methods SDSM and LARS-WG and a newly developed MLR-downscaling technique that allows the incorporation of a multitude of monthly or daily climate predictors from one- or several (multi-domain) parent GCMs. The numerous downscaling experiments indicate that the MLR- method is more accurate than SDSM and LARS-WG in predicting the recent past 20th-century (1971-2000) long-term monthly climate in the region. The MLR-model is, consequently, then employed to downscale 21st-century GCM- climate predictions under SRES-scenarios A1B, A2 and B1. However, since the hydrological watershed model requires daily-scale climate input data, a new stochastic daily climate generator is developed to rescale monthly observed or predicted climate series to daily series, while adhering to the statistical and geospatial distributional attributes of observed (past) daily climate series in the calibration phase. Employing this daily climate generator, 30 realizations of future daily climate series from downscaled monthly GCM-climate predictor sets are produced and used as input in the SWAT- distributed watershed model, to simulate future streamflow and other hydrological water budget components in the study region in a multi-realization manner. In addition to a general examination of the future changes of the hydrological regime in the KY-basin, potential future changes of the water budgets of three main reservoirs in the basin are analysed, as these are a major source of water supply in the study region. The results of the long-term 21st-century downscaled climate predictions provide evidence that, compared with the past 20th-reference period, the future climate in the study area will be more extreme, particularly, for SRES A1B. Thus, the temperatures will be higher and exhibit larger fluctuations. Although the future intensity of the rainfall is nearly constant, its spatial distribution across the region is partially changing. There is further evidence that the sequential rainfall occurrence will be decreased, so that short periods of high intensities will be followed by longer dry spells. This change in the sequential rainfall pattern will also lead to seasonal reductions of the streamflow and seasonal changes (decreases) of the water storage in the reservoirs. In any case, these predicted future climate changes with their hydrological impacts should encourage water planner and policy makers to develop adaptation strategies to properly handle the future water supply in this area, following the guidelines suggested in this study.

Finding Spaces for Urban Food Production – Matching Spatial and Stakeholder Analysis with Urban Agriculture Approaches in the Urban Renewal Area of Dortmund-Hörde, Germany

Using the case of an economically declined neighbourhood in the post-industrial German Ruhr Area (sometimes characterized as Germany’s “Rust Belt”), we analyse, describe and conclude how urban agriculture can be used as a catalyst to stimulate and support urban renewal and regeneration, especially from a socio-cultural perspective. Using the methodological framework of participatory action research, and linking bottom-up and top-down planning approaches, a project path was developed to include the population affected and foster individual responsibility for their district, as well as to strengthen inhabitants and stakeholder groups in a permanent collective stewardship for the individual forms of urban agriculture developed and implemented. On a more abstract level, the research carried out can be characterized as a form of action research with an intended transgression of the boundaries between research, planning, design, and implementation. We conclude that by synchronously combining those four domains with intense feedback loops, synergies for the academic knowledge on the potential performance of urban agriculture in terms of sustainable development, as well as the benefits for the case-study area and the interests of individual urban gardeners can be achieved.